The Interaction between Photonics and Machine Learning

A special issue of Photonics (ISSN 2304-6732).

Deadline for manuscript submissions: 30 June 2025 | Viewed by 868

Special Issue Editors


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Guest Editor
Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China
Interests: photonic-integrated circuits; photonics intelligent sensing; semiconductor lasers; artificial intelligence; machine learning; reinforcement learning
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: LiDAR (light detection and ranging); photonics sensor; deep learning; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue dedicated to "The Interaction between Photonics and Machine Learning". Machine learning has transformed various domains by extracting valuable insights from complex data. Photonics and machine learning can be combined in two ways: one involves utilizing machine learning algorithms as optimization methods or fitting techniques instead of the mathematical modeling and computer simulation to solve optical problems (machine learning for photonics), while the other leverages the higher computational frequencies and the lower energy consumption of photonics to perform machine learning tasks (photonics for machine learning). This Special Issue aims to explore the intersection between photonics and machine learning, highlighting their synergistic relationship and the potential for groundbreaking advancements in various fields.

Dr. Guohui Yuan
Prof. Dr. Zhuoran Wang
Guest Editors

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Keywords

  • computational optics and machine learning
  • deep learning for optical systems
  • AI-empowered photonic sensor networks
  • adaptive optics and machine learning algorithms
  • optoelectronic neural networks for information processing
  • machine learning-based optimization of photonic devices
  • photonics-enabled machine vision systems
  • AI-driven design of optical components
  • quantum photonics and quantum machine learning
  • reinforcement learning to control optical systems

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Published Papers (1 paper)

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Research

22 pages, 7818 KiB  
Article
A Novel Two-Stage Approach for Nonlinearity Correction of Frequency-Modulated Continuous-Wave Laser Ranging Combining Data-Driven and Principle-Based Strategies
by Shichang Xu, Guohui Yuan, Hongwei Zhang, Chunyu Hou, Zhirong Li, Pansong Zhang, Wenhao Xu and Zhuoran Wang
Photonics 2025, 12(4), 356; https://doi.org/10.3390/photonics12040356 - 9 Apr 2025
Viewed by 305
Abstract
The frequency linearity of a frequency-swept light signal is critical for ensuring the precision of Frequency-Modulated Continuous-Wave (FMCW) laser ranging systems. A two-stage nonlinearity correction mechanism for frequency-swept light is proposed, combining both data-driven and principle-based approaches. In the main correction stage utilizing [...] Read more.
The frequency linearity of a frequency-swept light signal is critical for ensuring the precision of Frequency-Modulated Continuous-Wave (FMCW) laser ranging systems. A two-stage nonlinearity correction mechanism for frequency-swept light is proposed, combining both data-driven and principle-based approaches. In the main correction stage utilizing an electro-optic phase-locked loop (EO-PLL), high temporal resolution phase detection is achieved. To address the failure of the EO-PLL caused by a bandwidth limitation of the digital loop filter (DLF), a novel pre-correction mechanism is developed based on a data-driven approach. In this mechanism, the neural network (NN) model establishes a mapping relationship between the input and output of the real laser-modulation system, which effectively simulates this physical system and avoids the risk of trial-and-error damage. Afterwards, the Soft Actor–Critic (SAC) model interacts with the NN model and trains a decision-making agent to determine the optimal modulation strategy for the nonlinearity pre-correction of the frequency-swept light. During the training process of the SAC agent, both the modulation strategy and the accuracy of evaluating the strategy’s effectiveness are optimized. Moreover, in contrast to the basic Actor–Critic model, the SAC model enhances the exploration of modulation possibilities by maximizing entropy expectation of random strategy, thereby improving the robustness of the pre-correction mechanism. Finally, the frequency-swept characteristic analysis experiment proves that integrating NN-SAC with EO-PLL enables frequency locking under the reduced bandwidth of the DLF. Additionally, through actual ranging experiments, it is also demonstrated that the proposed mechanism significantly enhances ranging precision, repeatability, and stability. Therefore, by integrating data-driven and principle-based approaches, this investigation offers an innovative perspective for the nonlinearity correction of FMCW laser ranging and, furthermore, electro-optic control scenarios. Full article
(This article belongs to the Special Issue The Interaction between Photonics and Machine Learning)
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